Transaction range comparison for financial investigation

ABSTRACT

Systems and methods for determining the likelihood that a group of transactions may be structured to avoid a limit or reporting requirement, such as a government reporting requirement, are disclosed. The frequency distributions of a customer&#39;s transactions for different value ranges are compared to determine whether transactions within a target range occur randomly or at an unexpected level. In another embodiment, the frequency distribution of a customer&#39;s transactions is compared to a frequency distribution created by randomly sampling a distribution of similar transactions to determine whether the customer&#39;s transactions occur randomly.

FIELD OF THE INVENTION

Embodiments of the present invention relate to systems and methods thatare utilized to identify transactions that are potentially related tofinancial misconduct. More particularly, embodiments of the inventionprovide mechanisms for determining whether a group of transactions arelikely random or structured to circumvent government reportingrequirements.

DESCRIPTION OF THE RELATED ART

Financial institutions monitor customer transactions in an effort toidentify money laundering activities. Money Laundering is the practiceof filtering the proceeds of illegitimate activity through a series ofseemingly legitimate transactions to conceal or obscure the illegitimateorigin of the funds involved in the transactions. One method of moneylaundering involves structuring transactions to avoid governmentreporting requirements. Currently in the United States, transactionsthat involve more than $10,000 in currency, i.e., cash must be reportedto the government. Structuring occurs, for example, when a financialinstitution customer makes multiple withdrawals or deposits that areeach below the reporting threshold, but when combined, exceed thereporting threshold. For example, a person who wishes to deposit $13,000cash may make a first deposit of $8,000 and a second deposit of $5,000in an attempt to avoid the reporting requirements.

Financial institutions report transactions that appear to be of asuspicious nature to investigatory entities. The amount of time andresources expended by financial institutions and investigatory entitiescan be considerable because it is often difficult and time consuming toreview raw financial data and accurately determine whether or not acustomer is structuring transactions to avoid government reportingrequirements.

Therefore, there exists a need in the art for systems and methods thatassist financial institutions in determining whether or not transactionsare potentially related to money laundering activities.

SUMMARY OF THE INVENTION

Aspects of the invention overcome at least some of the problems andlimitations of the prior art by providing systems and methods that maybe used to determine the likelihood that a group of transactions arestructured to avoid government reporting requirements. In a firstembodiment, transaction activity data are sampled to create a frequencydistribution that is compared to a customer's transaction data within atarget range to determine whether the customer's transactions occurrandomly. In another embodiment, the distribution of a customer'stransactions among different value ranges are compared to determinewhether transactions within the target range occur at a random or higherrate.

Of course, the methods and systems disclosed herein may also includeother additional elements, steps, computer-executable instructions, orcomputer-readable data structures. The details of these and otherembodiments of the present invention are set forth in the accompanyingdrawings and the description below. Other features and advantages of theinvention will be apparent from the description and drawings, and fromthe claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may take physical form in certain parts and steps,embodiments of which will be described in detail in the followingdescription and illustrated in the accompanying drawings that form apart hereof, wherein:

FIG. 1 illustrates a method of determining the likelihood that a groupof financial transactions are structured to avoid a limit or reportingrequirement, such as a government reporting requirement, in accordancewith an embodiment of the invention.

FIG. 2 shows a table that compares data under test to randomly sampleddata, in accordance with an embodiment of the invention.

FIG. 3 illustrates an alternative method that may be used to determinethe likelihood that an entity may be structuring financial transactionsto avoid a limit or reporting requirement, in accordance with anembodiment of the invention.

FIG. 4 illustrates an exemplary user interface that may be used to settarget and adjacent ranges, in accordance with an embodiment of theinvention.

FIG. 5 illustrates a table of frequency distributions within a targetrange and two adjacent ranges, in accordance with an embodiment of theinvention.

DETAILED DESCRIPTION

Aspects of the present invention are preferably implemented withcomputer devices and computer networks that allow users to exchange andprocess financial transaction data. Each computer device may include avariety of conventional hardware and software components. Exemplarycomponents include: magnetic memory modules, physical memory modules, anetwork card, a modem, a central processor that controls the overalloperation of the computer and a system bus that connects the centralprocessor to one or more conventional hardware components. Each computerdevice may also include a variety of interface units and drives forreading and writing data or files. Depending on the type of computerdevice, a user can interact with the computer with a keyboard, pointingdevice, microphone, pen device or other input device. The operations ofcomputer devices may be controlled by computer-executable instructionsstored on computer-readable medium.

FIG. 1 illustrates a method of determining the likelihood that a groupof financial transactions are structured to avoid a limit or reportingrequirement, such as a government reporting requirement, in accordancewith an embodiment of the invention. First, in step 102 a collection oftransaction activity data is collected for at least one entity. Thetransaction activity data may include deposit and withdrawal activity ata financial institution over a predetermined period of time, such as ayear.

In step 104 the transaction activity data is randomly sampled. Step 104may be performed to create a distribution of data that may be usedlater. In one embodiment, sampled data is created by simulating a set ofweeks of random activity by sampling the raw transaction data a largenumber of times. A group of synthetic weeks may be created by groupingtogether seven days selected at random. The process may be repeateduntil enough synthetic weeks are created to cover a predetermined timeperiod.

Activity data under test that represents an entity's is received in step106. The activity data under test may include an entity's deposit andwithdrawal activity at a financial institution over a predeterminedperiod of time, such as a year.

Next, in step 108 the sampled transaction activity data is compared toactivity data under test to determine the likelihood that the activitydata under test is random or includes transactions structured to avoid alimit or reporting requirement, such as a government reportingrequirement. FIG. 2 shows a table that compares data under test to datacreated through the random sampling of data. In the embodiment shown,column 202 includes a number of transactions. Column 204 shows thenumber of weeks that the data under test has the indicated number oftransactions within a target range. For example, column 204 indicatesthat there were 39 weeks in which 0 transactions occurred within thetarget range and there were 17 weeks in which 1 transaction occurredwithin the target range. Column 206 shows the number of synthetic weeksfor which the data includes the indicated number of transactions withinthe target range. The data shown in column 206 may have been or can benormalized by dividing the relevant counts by the number entities.Column 206 shows that there were 30 synthetic weeks in which 0transactions occurred within the target range and 16 synthetic weeks inwhich 1 transaction occurred within the target range.

In one implementation of step 108, statistical analysis may be performedto determine whether the frequency distribution shown in column 204 isstatistically different from the frequency distribution shown in column206. Those skilled in the art will appreciate that a number ofstatistical procedures may be performed to indicate this difference. Inone embodiment, a Chi-square goodness-of-fit test is utilized.Chi-square goodness-of-fit tests are well known to those skilled in theart. When the results of the chi-square goodness-of-fit test exceeds areferenced, predetermined value, the distributions in columns 204 and206 are considered to be significantly different, which suggests thattransactions are not occurring randomly and may be an indication thatthe transactions are being structured to avoid a limit or reportingrequirement, such as a government reporting requirement.

FIG. 3 illustrates an alternative method that may be used to determinethe likelihood that an entity may be structuring financial transactionsto avoid a limit or reporting requirement, such as a governmentreporting requirement, in accordance with an embodiment of theinvention. First, in step 302 financial transaction data relating to atleast one entity's financial transactions involving at least onefinancial institution is received. Step 302 may include receiving datadescribing all of the deposits and withdrawals for a customer of afinancial institution. In various alternative embodiments, thetransactions of more than one entity, such as a group of members of thesame household, may be used when performing the analysis. Moreover, thedata for transactions involving more than one financial institution mayalso be combined when performing the analysis. Multiple entities andfinancial institutions may be used when trying to locate complex moneylaundering schemes.

Next, in step 304 a frequency distribution for financial transactionsthat involve dollar amounts that fall within a target range isdetermined FIG. 4 illustrates an exemplary user interface that may beused to set ranges that are used with the method shown in FIG. 3. Icons402, 404, 406 and 408 are moveable along a horizontal axis parallel tothe scale showing dollar amounts. In one embodiment, the governmentrequires transactions that involve more than $10,000 cash be reported. Atarget range may correspond to the values between $8,000 and $10,000. Ofcourse, the user may slide icon 404 to the left or to the right toadjust the lower end of the target range. Icon 406 may be moved to theleft or right to adjust the upper end of the target range.

An upper range and/or a lower range may be defined to facilitatedetermining whether or not transactions that fall within the targetrange occur at an expected level (or volume). In the embodiment shown,icon 408 is adjusted to $12,000 to create an upper range between $10,000and $12,000. Similarly, a lower range is established between $6,000 and$8,000. One skilled in the art will appreciate that a variety ofdifferent user interface elements and other mechanisms may be used toestablish the range values shown in FIG. 4. For example, a userinterface may include text blocks that allow a user to directly enterthe values. In one particular embodiment, the values entered directlyinto a spreadsheet document.

Returning to FIG. 3, in step 306 a second frequency distribution forfinancial transactions that involve dollar amounts that fall within adifferent range is determined. In one embodiment, the range is adjacentto the target range. FIG. 5 illustrates a table of frequencydistributions within a target range and two adjacent ranges, inaccordance with an embodiment of the invention. A first column 502indicates a number of transactions. Column 504 shows the number of weekswith the indicated number of transactions that occur in the lower range.An exemplary lower range includes transactions having values between$6,000 and $8,000. Column 506 shows the number of weeks having theindicated number of transactions within the target range. An exemplarytarget range includes transactions having values between $8,000 and$10,000. Column 506 shows, for example, that there were 8 weeks with 1transaction in the target range. Column 508 shows the number of weekshaving the indicated number of transactions within an upper range. Anexemplary upper range includes transactions having values between$10,000 and $12,000. Column 508, for example, shows that there were 50weeks with 0 transactions in the upper range.

Again returning to FIG. 3, finally in step 308 the first frequencydistribution is compared to the second frequency distribution todetermine the likelihood that an entity structured financialtransactions to avoid a reporting requirement. The reporting requirementmay be a government reporting requirement. In alternative embodiments,multiple frequency distributions are used in the comparison. Forexample, the frequency distribution of transactions that fall within atarget range may be compared to 2, 3 or 4 frequency distributions fortransactions that fall within other ranges.

One skilled in the art will appreciate that the methods shown in FIGS. 1and 3 may be combined. In one embodiment, a single software tool may beconfigured to implement both of the illustrated methods or allow a userto choose which method to implement.

The present invention has been described herein with reference tospecific exemplary embodiments thereof. It will be apparent to thoseskilled in the art that a person understanding this invention mayconceive of changes or other embodiments or variations, which utilizethe principles of this invention without departing from the broaderspirit and scope of the invention as set forth in the appended claims.All are considered within the sphere, spirit, and scope of theinvention.

1. An apparatus for determining that a group of transactions may bestructured to avoid a predetermined reporting requirement, the apparatuscomprising: a memory; and a processor configured to retrievecomputer-executable instructions from the memory and to perform:receiving a collection of transaction activity data involving financialinstitution transactions; sampling the transaction activity data over asynthesized period of time by randomly grouping together componentperiods of time included in the synthesized period of time; receivingunsampled activity data under test that represents transactions for afirst financial entity and for a second financial entity; combining theunsampled activity data for the first financial entity and the secondfinancial entity; comparing the sampled transaction activity data to thecombined unsampled activity data under test to determine a likelihoodthat the unsampled activity data under test is occurring randomly; andbased on the likelihood, determining whether a group of transactions fordifferent individuals and for the first financial entity and the secondfinancial entity is structured to avoid a predetermined reportingrequirement.
 2. The apparatus of claim 1, wherein the synthesized periodof time comprises a set of weeks.
 3. The apparatus of claim 1, whereinthe collection of transaction activity data comprises raw transactiondata for customers of a financial institution.
 4. The apparatus of claim2, wherein the sampling comprises sampling raw transaction data a numberof times to create simulated transaction activity for the set of weeks.5. The apparatus of claim 1, wherein the comparing comprises performingstatistical analysis to compare a frequency distribution of the sampledtransaction activity data to a frequency distribution of the unsampledactivity data under test.
 6. The apparatus of claim 5, wherein thecomparing comprises performing a chi-square goodness-of-fit test.
 7. Theapparatus of claim 1, wherein the different individuals comprise peoplewho live in the same household.
 8. The apparatus of claim 1, wherein theunsampled activity data under test comprises financial data from morethan one financial institution and wherein the processor furtherperforms combining the financial data from the more than one financialinstitution.
 9. The apparatus of claim 1, wherein the predeterminedreporting requirement comprises a government reporting requirement thatrequires the reporting of deposits and withdrawals exceeding apredetermined value.
 10. A computer-readable medium containingcomputer-executable instructions for causing a computer device toperform the steps comprising: providing a collection of transactionactivity data involving financial institution deposits and withdrawals;sampling the transaction activity data over a synthesized period of timeby randomly grouping together component periods of time included in thesynthesized period of time; combining unsampled activity for activityfor a first financial entity and a second financial entity; comparingthe sampled transaction activity data to the combined unsampled activitydata under test to determine a likelihood that the unsampled activitydata under test is occurring randomly; and based on the likelihood,determining whether a group of transactions for different individualsand for the first financial entity and the second financial entity isstructured to avoid a government reporting requirement.
 11. Acomputer-readable medium of claim 10, the computer-executableinstructions further causing the computer device to perform: performinga statistical analysis to compare a frequency distribution of thesampled transaction activity data to a frequency distribution of theunsampled activity data under test.
 12. A computer-readable medium ofclaim 11, the computer-executable instructions further causing thecomputer device to perform: performing a chi-square goodness-of-fittest.
 13. A computer-readable medium of claim 11, thecomputer-executable instructions further causing the computer device toperform: combining financial data from more than one financialinstitution.